The Best Local Voice-to-Text Models in 2026 (I Tested Them Building a Dictation App)

I build a voice typing app. Full disclosure up front: it's called Meander, so read everything here with that bias in mind. When you build one of these, the first thing everyone asks is "why not just run it locally? No cloud, no privacy worries, no bills." It's a fair question. So I spent a while putting the local speech-to-text models I could run through real dictation, with one rule that ruled a lot of them out: it had to handle Chinese.

Here's the honest map of what I found, including the one thing that quietly changed how I think about the whole local-versus-cloud question.

Start with Whisper, because everyone does

OpenAI's Whisper is the default. I tested the lineup on real dictation:

• Base. Tiny and fast, but the accuracy is rough. Fine for a throwaway note, frustrating the moment you want clean text.
• Large-v3-turbo. A distilled version, roughly 4x faster than the full large model. One catch: turbo drops Whisper's translation task entirely. For plain dictation, this is the one I'd pick.
• Large-v3. The biggest of the family, and the one topping most published benchmarks on clean audio. On real, messy dictation it surprised me: when I corrected myself mid-sentence, it tended to write down only the corrected version. Turbo wrote what I said.

The benchmarks line up with this. Large-v3 lands around 2.7% word error rate on clean audio and wants about 3 GB of VRAM (model comparison, Hugging Face). When we later timed both on real dictation, turbo came out ahead on speed and accuracy. The full numbers are in our local vs cloud benchmark.

If you need Chinese, the Whisper-only story falls apart

Most "best local model" roundups stop at Whisper. But the moment you need anything other than English, the picture changes, and this is where my testing got interesting.

• NVIDIA Parakeet. Blazing fast, accuracy on par with Whisper large for English. The newer V3 added a batch of European languages, but here's the catch no benchmark headline mentions: still no Chinese, and the fastest one (V2) is English-only. For a product that needs CJK, fast doesn't help if the language simply isn't there (benchmarks).
• SenseVoice (Alibaba's FunASR). This one impressed me. It's non-autoregressive, so it's fast (the small model is about 15x faster than Whisper-large), it's small, and it handles Chinese, Cantonese, Japanese, and Korean well. For local Chinese dictation, it's the one I kept coming back to (GitHub).
• Breeze ASR (MediaTek Research). A Whisper-large-v2 fine-tune built for Taiwanese Mandarin and Mandarin-English code-switching, the everyday habit of mixing Chinese and English mid-sentence. It improves code-switching by over 50% versus stock Whisper. Niche, but if that's how you actually talk, nothing else comes close (GitHub).
• Cohere Transcribe (Cohere Labs). The accuracy champion of the bunch. It's a 2B-parameter open model (Apache 2.0) that currently tops the Open ASR Leaderboard and beats Whisper Large-v3 (about 5.4% word error rate to Whisper's 7.4% on English), across 14 languages including Mandarin, Japanese, and Korean. The trade-off in practice: it's heavier and slower than the tiny models above, so you pay in speed for that accuracy (release notes).

There's a whole tail of smaller, single-region models too: Moonshine for English, Canary for European languages plus translation, GigaAM for Russian. All worth a look if Chinese isn't on your list, and most of them are tiny (some under 100 MB).

The part that changed my mind: the model isn't the bottleneck anymore

Speech recognition got small and good. SenseVoice is tiny and fast. So I expected local to be easy from here. It wasn't, and the reason caught me off guard.

A voice typing app isn't just transcription. It's transcribe, then run an AI cleanup pass to fix grammar and filler words. That cleanup is a second model: an LLM. And the LLM is the hog.

Take SenseVoice, the model I kept reaching for. On its own it barely touches 1 GB of VRAM. The recognition side is basically a solved problem now, and the cost-performance is hard to argue with. Then you add the cleanup LLM, and even a modest one runs 3 to 6 GB (a small Gemma or Qwen, say). Stack the two, add the model's context and the usual overhead, and on my 16 GB card the whole thing sat at 8 to 10 GB. The operating system and whatever else I had open took a couple more. Suddenly there was nothing left. I couldn't run ComfyUI. I couldn't load another model. A voice input method had eaten my entire graphics card just sitting there, waiting for me to talk.

That's the real local tax, and it stopped being about the speech model a while ago. It's the whole stack.

And it isn't only the memory. The small LLMs that fit on a consumer card are quick, but they make unreliable editors. Ask one to fix grammar and strip filler and it will sometimes ignore the instruction, rewrite more than you wanted, or behave differently every time. The models that do clean, predictable cleanup are big, a 120B-class model like OpenAI's gpt-oss, and you are simply not running that on a desktop. So local cleanup turns into a no-win: the model that fits isn't good enough, and the one that's good enough doesn't fit. That is the part that pushed me to the cloud, where you can reach the big model without having to own the big machine.

(Which local LLM to pick, and how to fit it, is a rabbit hole of its own, probably its own post someday. The short version is the one above: small enough to run usually means not reliable enough to trust.)

So, local or cloud?

Local is good, and free, if two things are true: you have stable, sufficient hardware, and you're willing to give that GPU to it. If your machine has the headroom, or you offload the models to a separate GPU box, run local, keep your audio on your own hardware, and pay nothing. For privacy-hard requirements, this is the only real answer.

For everyone else the math is harder. A dedicated GPU is expensive, and handing your whole card to an input method so it can listen is a steep price to pay. That's where the cloud wins: someone else runs the big, accurate models, your machine stays free, and long recordings come back fast.

I went down the local road far enough to know it well, and that is why Meander now gives you both roads. The desktop app can run speech recognition and AI cleanup in the cloud or on your own machine, per mode, and you can mix the two. The models in this article, Whisper turbo and Breeze included, are in its built-in model library. If you want to know how long each option makes you wait, we measured it. If you're on Linux, here's how to set Meander up on Wayland.

At a glance

The speech models themselves are all small (most are well under 1 GB to download). The memory that bites comes later, when the cleanup LLM stacks on top, and that is what filled my 16 GB card.

The honest takeaway

Don't choose a local voice setup based on the speech model alone. The model is the easy part now. The real question is whether you want to spend your GPU, your time, and your machine's headroom to keep everything offline. If you do, the open models above are good, and you should use them. If you'd rather your computer stay yours, the cloud is the simpler trade.